(1) Field of the Invention
The present invention relates to a diagnostics system and more particularly to an enterprise diagnostic system, based on distributed objects and Internet Inter-ORB protocol (IIOP), for diagnosing faults in avionics systems.
(2) Description of Related Art
Explosive growth in technology has made possible increasingly complex avionics systems for use in aviation, missilery and astronautics. In particular, the development of electrical and electronic devices have ushered in an era where avionics systems now assist the flight crew in virtually every aspect of operating modern aircraft.
Reliable operation of the aircraft therefore depends on avionics systems that not only perform their intended functions but are also reliable. In operation, however, vibration, rapid attitude changes, rapid environmental changes and other similar influences can affect performance and reliability of the avionics system. By way of example, the avionics system may include electrical and electronic sub-systems having an intended use in communications, navigation, aircraft control, remote sensing (such as the amount of on-board fuel), wiring and power distribution, life support, electronic warfare equipment (in military aircraft and missilery) and various sensors that provide vital real-time information. Accordingly, those concerned with flying and maintaining the aircraft, in particular, require methods of verifying the correct operation of the avionics systems both in-flight and during maintenance periods. For this reason modem aircraft include a real-time diagnostic system to monitor the avionics and other systems.
It will be appreciated that system level diagnostics of avionics systems are necessarily complex especially in view of the potential for interaction between the various sub-systems. Due to this interaction and complexity, diagnostic capability has, in the past, been tightly coupled to the system definition. This tight coupling makes it difficult to improve or upgrade the diagnostic system thereby rendering prior art diagnostic systems obsolete as the avionics system is improved. Accordingly, there is a great need to provide a method for updating diagnostic systems concurrently with the improvement or upgrade of the avionics system in an efficient and inexpensive manner.
Modern commercial aircraft (such as the Boeing 747 and the Boeing 777 models provided by the Boeing Company) include diagnostic systems based around a central maintenance computer (CMC). This system collects, processes and evaluates avionics system information to verify normal operation by comparing sensor inputs against pre-defined rules to detect operational failures.
For example, the diagnostic system on the Boeing 747-400 aircraft uses loosely structured Boolean logic equations to perform its diagnostic evaluation. The primary disadvantage of this diagnostic system stems from the complexity of the rules (i.e., logic equations) and the difficulties engineers have in maintaining the rules over the life of the aircraft. Another disadvantage suffered using this approach is that diagnostic accuracy is limited by lack of standards for the various sub-systems that comprise the avionics system and by lack of large-scale system simulation capability. Unfortunately, even though the implementation of the diagnostic system on the 747-400 took a significant amount of time and expense to mature, its adaptation or implementation on other aircraft is not possible. Thus, this time and expense must be incurred for each type or model of aircraft. Further, upgrades or additions to the avionics system during the life of the aircraft often require additional expensive and time-consuming effort to update the model to reflect the changes to the diagnostic system.
To overcome the problems associated with the above-described diagnostic system, the Boeing 777 aircraft adopted a shallow model-based approach based on a simple relational database structure. This diagnostic system segments the primary diagnostic functions such as signal validation, cascade-effect removal and fault isolation and correlation into a series of sequentially processed tasks. This approach, however, is limited since the shallow low-fidelity model paradigm focuses on reporting faults rather than evaluating physical system-level failure characteristics. Further, this model-based approach does not easily support analysis of the complex relationships among fault signals.
Another shortcoming of the Boeing 777 diagnostic system is that it is text based. Thus, it offers very limited design or analysis capability and offers no simulation capability. Further still, this diagnostic system offers only limited fault coverage leading to large ambiguity groups and "no fault found" conditions for line replaceable units (LRUs) pulled from the aircraft during the repair effort.
If changes are made to the avionics systems, both of the diagnostic systems described above must regenerate an entirely new model or set of rules, an expensive and technically demanding process. Traditional avionics system diagnostic system architecture tightly couples diagnostic interfaces (such as graphic displays, control panels, sensor inputs, keyboards) data storage and retrieval, and the application code in a single black box. This architecture provides for a focused diagnostic system that meets the diagnostic requirements of one avionics system. However, such diagnostic systems are difficult to modify, expand or upgrade since it requires detailed knowledge of the entire avionics system. Further, the process of modifying the model or amending the rules to incorporate the changes to the avionics system is a non-trivial task that is time consuming, expensive and susceptible to error. Further still, such diagnostic systems do not provide any system level visibility of how sub-systems work together so it is difficult for pilots and ground crew to visualize the overall system and the inter-relationship of the various sub-systems. This lack of system level visibility makes trouble shooting unnecessarily complicated, expensive and time consuming.
Few current diagnostic systems provide the capability to model an avionics system on a system level incorporating the design of various sub-systems from many different manufacturers into a cohesive model. Further, no current diagnostic system provides the capability to perform detailed simulation and diagnostic testing of the avionics system to guarantee rapid generation of a rule set that fully defines all potential failure modes. Further still, no current diagnostic system provides real-time diagnostic inference of avionics system failure in a visual manner so that the inter-relationship between various sub-systems is readily apparent to both the flight crew and a ground based maintenance crew. Thus, whatever the merits of the above described prior art diagnostic systems for avionics systems, they do not achieve the benefits of the present invention. Clearly, what is needed is a diagnostic system for avionics systems that provides greater flexibility, expansion, and maintainability than prior art solutions. What is also needed is a user-friendly system for modeling the interaction of the various sub-systems comprising an avionics system and for diagnosing system failure modes based on this model. Further, since the available memory and diagnostic computing power is typically limited on board an aircraft, it is desirable that the diagnostic system consume a minimum of computer resources while at the same time providing full coverage of the potential failure modes of the avionics system.